Design research and education focused on developing ethical engineers.
Ethical risk is not only defined by the systems' architect but also by the end user.
Adapted heuristic from The Art of Systems Architecting by Maier & Rechtin, 2009
The Global SAS survey of 2200 business leaders and managers shows a broad awareness of the risks inherent in using AI, but few practitioners have taken action to create policies and processes to manage risks, including ethical, legal, reputational, and financial risks. Managing ethical risk is a particular area of opportunity. Companies with more advanced AI practices are establishing processes and policies for ethical software engineering practices, data governance and risk management, including providing ways to explain how algorithms work and deliver results. These leaders point out that understanding how AI systems reach their conclusions is both an emerging best practice and an ethical necessity, in order to ensure that the human intelligence that feeds and nurtures AI systems keeps pace with the machines’ advancements (adapted from MIT SMR Connections/SAS. (2020). How AI Changes the Rules: New Imperatives for Intelligent Organizations.)
A critical evaluation, an ethicalscope™ helps universities assess ethical benchmarks and milestones in their computer engineering curricula. See one sample evaluation: